PubMed · 10503511
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N Ohya. 1999. [Galactose].. https://pubmed.ncbi.nlm.nih.gov/10503511/
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Lithium is highly toxic to yeast when grown in galactose medium mainly because phosphoglucomutase, a key enzyme of galactose metabolism, is inhibited. We studied the global protein and gene expression profiles of Saccharomyces cerevisiae grown in galactose in different time intervals after addition of lithium. These results were related to physiological studies where both secreted and intracellular metabolites were determined. Microarray analysis showed that 664 open reading frames were down-regulated and 725 up-regulated in response to addition of lithium. Genes involved in transcription, translation, and nucleotide metabolism were down-regulated at the transcriptional level, whereas genes responsive to different stresses as well as genes from energy reserve metabolism and monosaccharide metabolism were up-regulated. Compared with the proteomic data, 26% of the down-regulated and 48% of the up-regulated proteins were also identified as being changed on the mRNA level. Functional clusters obtained from proteome data were coincident with transcriptional clusters. Physiological studies showed that acetate, glycerol, and glycogen accumulate in response to lithium, as reflected in expression data, whereas a change from respiro-fermentative to respiratory growth could not be predicted from the expression analyses.
A non-ionic water-soluble galactomannan, having galactose and mannose in 2:3 molar ratio was isolated from endosperm of the seeds of Ipomoea campanulata. The seed gum was found to have linear chain of beta (1-->4) linked mannopyranosyl units with D-galactose side chains attached through alpha (1-->6) linkage to the main chain. This structure is similar to many commercial gums like Guar, Carob and Locust bean gum. Various physical properties of the gum were studied in order to explore the possibility of commercial exploitation of the seed gum.
One of the grand challenges of system biology is to reconstruct the network of regulatory control among genes and proteins. High throughput data, particularly from expression experiments, may gradually make this possible in the future. Here we address two key ingredients in any such 'reverse engineering' effort: The choice of a biologically relevant, yet restricted, set of potential regulation functions, and the appropriate score to evaluate candidate regulatory relations. We propose a set of regulation functions which we call chain functions, and argue for their ubiquity in biological networks. We analyze their complexity and show that their number is exponentially smaller than all boolean functions of the same dimension. We define two new scores: one evaluating the fitness of a candidate set of regulators of a particular gene, and the other evaluating a candidate function. Both scores use established statistical methods. Finally, we test our methods on experimental gene expression data from the yeast galactose pathway. We show the utility of using chain functions and the improved inference using our scores in comparison to several extant scores. We demonstrate that the combined use of the two scores gives an extra advantage. We expect both chain functions and the new scores to be helpful in future attempts to infer regulatory networks.